package com.nunu.ai.nlp.controller;

import edu.stanford.nlp.coref.CorefCoreAnnotations;
import edu.stanford.nlp.coref.data.CorefChain;
import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
import edu.stanford.nlp.semgraph.SemanticGraph;
import edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.trees.TreeCoreAnnotations;
import edu.stanford.nlp.util.CoreMap;
import edu.stanford.nlp.util.PropertiesUtils;
import edu.stanford.nlp.util.StringUtils;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;

import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Properties;

/**
 * created with IDEA
 *
 * @author:huqm
 * @date:2020/12/7
 * @time:16:20 <p>
 *
 * </p>
 */
@RestController
@RequestMapping("/test")
public class TestController {


    @RequestMapping("getStr")
    public List<String> getStr(String text){

        return segInCh(text);
    }
    public List<String> segInCh(String text) {
        //载入properties 文件
//        StanfordCoreNLP pipline = new StanfordCoreNLP("StanfordCoreNLP-chinese.properties");

        //1.2 自定义功能 （1）
        //StanfordCoreNLP的各个组件（annotator）按“tokenize（分词）, ssplit（断句）, pos（词性标注）, lemma（词元化）,
        // ner（命名实体识别）, parse（语法分析）, dcoref（同义词分辨）”顺序进行处理。
//        Properties properties = new Properties();
//        properties.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
//        StanfordCoreNLP pipline = new StanfordCoreNLP(properties);

        //自定义功能(2) 自己在项目中建一个properties 文件，然后在文件中设置模型属性，可以参考1中的配置文件
//        String[] args = new String[]{"-props", "properies/CoreNLP-Seg-CH.properties"};
//        Properties properties = StringUtils.argsToProperties(args);
//        StanfordCoreNLP pipline = new StanfordCoreNLP(properties);

        //自定义功能(3)
        StanfordCoreNLP pipline = new StanfordCoreNLP(PropertiesUtils.asProperties(
                "annotators", "tokenize,ssplit,pos,lemma,ner,parse,dcoref",
                "ssplit.isOneSentence", "true",
                "tokenize.language", "zh",
                "segment.model", "edu/stanford/nlp/models/segmenter/chinese/ctb.gz",
                "segment.sighanCorporaDict", "edu/stanford/nlp/models/segmenter/chinese",
                "segment.serDictionary", "edu/stanford/nlp/models/segmenter/chinese/dict-chris6.ser.gz",
                "segment.sighanPostProcessing", "true"
        ));
        //创建一个解析器，传入的是需要解析的文本
        Annotation annotation = new Annotation(text);
        //解析
        pipline.annotate(annotation);
        //根据标点符号，进行句子的切分，每一个句子被转化为一个CoreMap的数据结构，保存了句子的信息()
        List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
        //从CoreMap 中取出CoreLabel List ,打印
        ArrayList<String> list = new ArrayList<>();
        for (CoreMap sentence : sentences) {
            for (CoreLabel token : sentence.get(CoreAnnotations.TokensAnnotation.class)) {
                String word = token.get(CoreAnnotations.TextAnnotation.class);
                 // this is the POS tag of the token
                String pos = token.get(CoreAnnotations.PartOfSpeechAnnotation.class);
                // this is the NER label of the token
                 String ne = token.get(CoreAnnotations.NamedEntityTagAnnotation.class);
                String lemma = token.get(CoreAnnotations.LemmaAnnotation.class);
                String result=word+"\t"+pos+"\t"+lemma+"\t"+ne;
                list.add(result);
            }
        }
        return list;
    }

}
